# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from itertools import cycle, islice
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn

import kornia.augmentation as K
from kornia.augmentation.base import _AugmentationBase
from kornia.contrib.extract_patches import extract_tensor_patches
from kornia.geometry.boxes import Boxes
from kornia.geometry.keypoints import Keypoints

from .base import SequentialBase
from .image import ImageSequential
from .ops import InputSequentialOps
from .params import ParamItem, PatchParamItem

__all__ = ["PatchSequential"]


class PatchSequential(ImageSequential):
    r"""Container for performing patch-level image data augmentation.

    .. image:: _static/img/PatchSequential.png

    PatchSequential breaks input images into patches by a given grid size, which will be resembled back
    afterwards.

    Different image processing and augmentation methods will be performed on each patch region as
    in :cite:`lin2021patch`.

    Args:
        *args: a list of processing modules.
        grid_size: controls the grid board separation.
        padding: same or valid padding. If same padding, it will F.pad to include all pixels if the input
            torch.Tensor cannot be divisible by grid_size. If valid padding, the redundant border will be removed.
        same_on_batch: apply the same transformation across the batch.
            If None, it will not overwrite the function-wise settings.
        keepdim: whether to keep the output shape the same as input (True) or broadcast it
            to the batch form (False). If None, it will not overwrite the function-wise settings.
        patchwise_apply: apply image processing args will be applied patch-wisely.
            if ``True``, the number of args must be equal to grid number.
            if ``False``, the image processing args will be applied as a sequence to all patches.
        random_apply: randomly select a sublist (order agnostic) of args to
            apply transformation.
            If ``int`` (batchwise mode only), a fixed number of transformations will be selected.
            If ``(a,)`` (batchwise mode only), x number of transformations (a <= x <= len(args)) will be selected.
            If ``(a, b)`` (batchwise mode only), x number of transformations (a <= x <= b) will be selected.
            If ``True``, the whole list of args will be processed in a random order.
            If ``False`` and not ``patchwise_apply``, the whole list of args will be processed in original order.
            If ``False`` and ``patchwise_apply``, the whole list of args will be processed in original order
            location-wisely.

    .. note::
        Transformation matrix returned only considers the transformation applied in ``kornia.augmentation`` module.
        Those transformations in ``kornia.geometry`` will not be taken into account.

    .. note::
        See a working example `here <https://kornia.github.io/tutorials/nbs/data_patch_sequential.html>`__.

    Examples:
        >>> import kornia.augmentation as K
        >>> input = torch.randn(2, 3, 224, 224)
        >>> seq = PatchSequential(
        ...     ImageSequential(
        ...         K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=0.5),
        ...         K.RandomPerspective(0.2, p=0.5),
        ...         K.RandomSolarize(0.1, 0.1, p=0.5),
        ...     ),
        ...     K.RandomAffine(360, p=1.0),
        ...     ImageSequential(
        ...         K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=0.5),
        ...         K.RandomPerspective(0.2, p=0.5),
        ...         K.RandomSolarize(0.1, 0.1, p=0.5),
        ...     ),
        ...     K.RandomSolarize(0.1, 0.1, p=0.1),
        ...     grid_size=(2,2),
        ...     patchwise_apply=True,
        ...     same_on_batch=True,
        ...     random_apply=False,
        ... )
        >>> out = seq(input)
        >>> out.shape
        torch.Size([2, 3, 224, 224])
        >>> out1 = seq(input, params=seq._params)
        >>> torch.equal(out, out1)
        True

    Perform ``OneOf`` transformation with ``random_apply=1`` and ``random_apply_weights`` in ``PatchSequential``.

        >>> import kornia
        >>> input = torch.randn(2, 3, 224, 224)
        >>> seq = PatchSequential(
        ...     ImageSequential(
        ...         K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=0.5),
        ...         K.RandomPerspective(0.2, p=0.5),
        ...         K.RandomSolarize(0.1, 0.1, p=0.5),
        ...     ),
        ...     K.RandomAffine(360, p=1.0),
        ...     K.RandomSolarize(0.1, 0.1, p=0.1),
        ...     grid_size=(2,2),
        ...     patchwise_apply=False,
        ...     random_apply=1,
        ...     random_apply_weights=[0.5, 0.3, 0.8]
        ... )
        >>> out = seq(input)
        >>> out.shape
        torch.Size([2, 3, 224, 224])

    """

    def __init__(
        self,
        *args: nn.Module,
        grid_size: Tuple[int, int] = (4, 4),
        padding: str = "same",
        same_on_batch: Optional[bool] = None,
        keepdim: Optional[bool] = None,
        patchwise_apply: bool = True,
        random_apply: Union[int, bool, Tuple[int, int]] = False,
        random_apply_weights: Optional[List[float]] = None,
    ) -> None:
        _random_apply: Optional[Union[int, Tuple[int, int]]]

        if patchwise_apply and random_apply is True:
            # will only apply [1, 4] augmentations per patch
            _random_apply = (1, 4)
        elif patchwise_apply and random_apply is False:
            if len(args) != grid_size[0] * grid_size[1]:
                raise ValueError(
                    "The number of processing modules must be equal with grid size."
                    f"Got {len(args)} and {grid_size[0] * grid_size[1]}. "
                    "Please set random_apply = True or patchwise_apply = False."
                )
            _random_apply = random_apply
        elif patchwise_apply and isinstance(random_apply, (int, tuple)):
            raise ValueError(f"Only boolean value allowed when `patchwise_apply` is set to True. Got {random_apply}.")
        else:
            _random_apply = random_apply
        super().__init__(
            *args,
            same_on_batch=same_on_batch,
            keepdim=keepdim,
            random_apply=_random_apply,
            random_apply_weights=random_apply_weights,
        )
        if padding not in ("same", "valid"):
            raise ValueError(f"`padding` must be either `same` or `valid`. Got {padding}.")
        self.grid_size = grid_size
        self.padding = padding
        self.patchwise_apply = patchwise_apply
        self._params: Optional[List[PatchParamItem]]  # type: ignore[assignment]

    def compute_padding(
        self, input: torch.Tensor, padding: str, grid_size: Optional[Tuple[int, int]] = None
    ) -> Tuple[int, int, int, int]:
        if grid_size is None:
            grid_size = self.grid_size
        if padding == "valid":
            ph, pw = input.size(-2) // grid_size[0], input.size(-1) // grid_size[1]
            return (-pw // 2, pw // 2 - pw, -ph // 2, ph // 2 - ph)
        if padding == "same":
            ph = input.size(-2) - input.size(-2) // grid_size[0] * grid_size[0]
            pw = input.size(-1) - input.size(-1) // grid_size[1] * grid_size[1]
            return (pw // 2, pw - pw // 2, ph // 2, ph - ph // 2)
        raise NotImplementedError(f"Expect `padding` as either 'valid' or 'same'. Got {padding}.")

    def extract_patches(
        self,
        input: torch.Tensor,
        grid_size: Optional[Tuple[int, int]] = None,
        pad: Optional[Tuple[int, int, int, int]] = None,
    ) -> torch.Tensor:
        """Extract patches from torch.Tensor.

        Example:
            >>> import kornia.augmentation as K
            >>> pas = PatchSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0), patchwise_apply=False)
            >>> pas.extract_patches(torch.arange(16).view(1, 1, 4, 4), grid_size=(2, 2))
            tensor([[[[[ 0,  1],
                       [ 4,  5]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[ 2,  3],
                       [ 6,  7]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[ 8,  9],
                       [12, 13]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[10, 11],
                       [14, 15]]]]])
            >>> pas.extract_patches(torch.arange(54).view(1, 1, 6, 9), grid_size=(2, 2), pad=(-1, -1, -2, -2))
            tensor([[[[[19, 20, 21]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[22, 23, 24]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[28, 29, 30]]],
            <BLANKLINE>
            <BLANKLINE>
                     [[[31, 32, 33]]]]])

        """
        if pad is not None:
            input = F.pad(input, list(pad))
        if grid_size is None:
            grid_size = self.grid_size
        window_size = (input.size(-2) // grid_size[-2], input.size(-1) // grid_size[-1])
        stride = window_size
        return extract_tensor_patches(input, window_size, stride)

    def restore_from_patches(
        self,
        patches: torch.Tensor,
        grid_size: Tuple[int, int] = (4, 4),
        pad: Optional[Tuple[int, int, int, int]] = None,
    ) -> torch.Tensor:
        """Restore input from patches.

        Example:
            >>> import kornia.augmentation as K
            >>> pas = PatchSequential(K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0), patchwise_apply=False)
            >>> out = pas.extract_patches(torch.arange(16).view(1, 1, 4, 4), grid_size=(2, 2))
            >>> pas.restore_from_patches(out, grid_size=(2, 2))
            tensor([[[[ 0,  1,  2,  3],
                      [ 4,  5,  6,  7],
                      [ 8,  9, 10, 11],
                      [12, 13, 14, 15]]]])

        """
        if grid_size is None:
            grid_size = self.grid_size
        patches_tensor = patches.view(-1, grid_size[0], grid_size[1], *patches.shape[-3:])
        restored_tensor = torch.cat(torch.chunk(patches_tensor, grid_size[0], 1), -2).squeeze(1)
        restored_tensor = torch.cat(torch.chunk(restored_tensor, grid_size[1], 1), -1).squeeze(1)

        if pad is not None:
            restored_tensor = F.pad(restored_tensor, [-i for i in pad])
        return restored_tensor

    def forward_parameters(self, batch_shape: torch.Size) -> List[PatchParamItem]:  # type: ignore[override]
        out_param: List[PatchParamItem] = []
        if not self.patchwise_apply:
            params = self.generate_parameters(torch.Size([1, batch_shape[0] * batch_shape[1], *batch_shape[2:]]))
            indices = torch.arange(0, batch_shape[0] * batch_shape[1])
            out_param = [PatchParamItem(indices.tolist(), p) for p, _ in params]
            # "append" of "list" does not return a value
        elif not self.same_on_batch:
            params = self.generate_parameters(torch.Size([batch_shape[0] * batch_shape[1], 1, *batch_shape[2:]]))
            out_param = [PatchParamItem([i], p) for p, i in params]
            # "append" of "list" does not return a value
        else:
            params = self.generate_parameters(torch.Size([batch_shape[1], batch_shape[0], *batch_shape[2:]]))
            indices = torch.arange(0, batch_shape[0] * batch_shape[1], step=batch_shape[1])
            out_param = [PatchParamItem((indices + i).tolist(), p) for p, i in params]
            # "append" of "list" does not return a value
        return out_param

    def generate_parameters(self, batch_shape: torch.Size) -> Iterator[Tuple[ParamItem, int]]:
        """Get multiple forward sequence but maximumly one mix augmentation in between.

        Args:
            batch_shape: 5-dim shape arranged as :math:``(N, B, C, H, W)``, in which N represents
                the number of sequence.

        """
        if not self.same_on_batch and self.random_apply:
            # diff_on_batch and random_apply => patch-wise augmentation
            with_mix = False
            for i in range(batch_shape[0]):
                seq, mix_added = self.get_random_forward_sequence(with_mix=with_mix)
                with_mix = mix_added
                for s in seq:
                    if isinstance(s[1], (_AugmentationBase, SequentialBase, K.MixAugmentationBaseV2)):
                        yield ParamItem(s[0], s[1].forward_parameters(torch.Size(batch_shape[1:]))), i
                    else:
                        yield ParamItem(s[0], None), i
        elif not self.same_on_batch and not self.random_apply:
            for i, nchild in enumerate(self.named_children()):
                if isinstance(nchild[1], (_AugmentationBase, SequentialBase, K.MixAugmentationBaseV2)):
                    yield ParamItem(nchild[0], nchild[1].forward_parameters(torch.Size(batch_shape[1:]))), i
                else:
                    yield ParamItem(nchild[0], None), i
        elif not self.random_apply:
            # same_on_batch + not random_apply => location-wise augmentation
            for i, nchild in enumerate(islice(cycle(self.named_children()), batch_shape[0])):
                if isinstance(nchild[1], (_AugmentationBase, SequentialBase, K.MixAugmentationBaseV2)):
                    yield ParamItem(nchild[0], nchild[1].forward_parameters(torch.Size(batch_shape[1:]))), i
                else:
                    yield ParamItem(nchild[0], None), i
        else:
            # same_on_batch + random_apply => location-wise augmentation
            with_mix = False
            for i in range(batch_shape[0]):
                seq, mix_added = self.get_random_forward_sequence(with_mix=with_mix)
                with_mix = mix_added
                for s in seq:
                    if isinstance(s[1], (_AugmentationBase, SequentialBase, K.MixAugmentationBaseV2)):
                        yield ParamItem(s[0], s[1].forward_parameters(torch.Size(batch_shape[1:]))), i
                    else:
                        yield ParamItem(s[0], None), i

    def forward_by_params(self, input: torch.Tensor, params: List[PatchParamItem]) -> torch.Tensor:
        in_shape = input.shape
        input = input.reshape(-1, *in_shape[-3:])

        for patch_param in params:
            # input, out_param = self.apply_by_param(input, params=patch_param)
            module = self.get_submodule(patch_param.param.name)
            _input = input[patch_param.indices]
            output = InputSequentialOps.transform(_input, module, patch_param.param, extra_args={})
            input[patch_param.indices] = output

        return input.reshape(in_shape)

    def transform_inputs(  # type: ignore[override]
        self, input: torch.Tensor, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> torch.Tensor:
        pad = self.compute_padding(input, self.padding)
        input = self.extract_patches(input, self.grid_size, pad)
        input = self.forward_by_params(input, params)
        input = self.restore_from_patches(input, self.grid_size, pad=pad)

        return input

    def inverse_inputs(  # type: ignore[override]
        self, input: torch.Tensor, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> torch.Tensor:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential inverse cannot be used with geometric transformations.")

    def transform_masks(  # type: ignore[override]
        self, input: torch.Tensor, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> torch.Tensor:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential for boxes cannot be used with geometric transformations.")

    def inverse_masks(  # type: ignore[override]
        self, input: torch.Tensor, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> torch.Tensor:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential inverse cannot be used with geometric transformations.")

    def transform_boxes(  # type: ignore[override]
        self, input: Boxes, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> Boxes:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential for boxes cannot be used with geometric transformations.")

    def inverse_boxes(  # type: ignore[override]
        self, input: Boxes, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> Boxes:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential inverse cannot be used with geometric transformations.")

    def transform_keypoints(  # type: ignore[override]
        self, input: Keypoints, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> Keypoints:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential for keypoints cannot be used with geometric transformations.")

    def inverse_keypoints(  # type: ignore[override]
        self, input: Keypoints, params: List[PatchParamItem], extra_args: Optional[Dict[str, Any]] = None
    ) -> Keypoints:
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential inverse cannot be used with geometric transformations.")

    def inverse(  # type: ignore[override]
        self,
        input: torch.Tensor,
        params: Optional[List[PatchParamItem]] = None,
        extra_args: Optional[Dict[str, Any]] = None,
    ) -> torch.Tensor:
        """Inverse transformation.

        Used to inverse a torch.Tensor according to the performed transformation by a forward pass, or with respect to
        provided parameters.
        """
        if self.is_intensity_only():
            return input

        raise NotImplementedError("PatchSequential inverse cannot be used with geometric transformations.")

    def forward(self, input: torch.Tensor, params: Optional[List[PatchParamItem]] = None) -> torch.Tensor:  # type: ignore[override]
        """Input transformation will be returned if input is a tuple."""
        # BCHW -> B(patch)CHW
        if isinstance(input, (tuple,)):
            raise ValueError("tuple input is not currently supported.")

        if params is None:
            params = self.forward_parameters(input.shape)

        output = self.transform_inputs(input, params=params)

        self._params = params

        return output
